Alexis halamka biography

As common as artificial intelligence has become in our daily lives — from Google’s Alexa, to Facebook’s data-driven ads, to Waze’s application of driver data to find the best route — it is little understood. The public tends to believe that AI comprehends complex problems and solves them the way humans do.

This myth can be blown up in an amusing fashion by reading posts at the blog aiweirdness, where Janelle Shane, an optics research scientist and AI researcher, writer, and public speaker, plays with machine learning algorithms to demonstrate that AI doesn’t get a lot of things humans instinctively grasp: candy heart messages, cat names (though “M. Tinklesby Linklater Soap” is oddly awesome), and most memorably, burlesque show names (“Deeptert!” and “Boodnass Tronpboons” stand out among the nonsensical weirdness).

Taking on three healthcare AI myths

In an article in Harvard Business Review, authors Derek A. Haas, Eric C. Makhni, Joseph H. Schwab, and John D. Halamka took on three myths of machine learning in healthcare.

• Myth #1: The perception that AI can replace doctors. While AI has an important role to play, for the foreseeable future AI will not replicate a doctor’s ability to provide care and treatment.
• Myth #2: Using “big data” in itself will lead to success. It’s true that more data is better, but only if it is the right data and it is fully understood.
• Myth #3: When an AI solution proves successful, it will be widely adopted and put to use. The fact is many powerful solutions are not accepted because they are not integrated into the workflow of potential users.

“The key is to be thoughtful about what types of problems AI is equipped to solve, who needs to be involved in developing the model and interpreting the output, and how to make it easy for people to utilize and act on the insights,” the authors say.

Make explicit decisions for the best outcomes

AllazoHealth’s AI engine is m

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 Welcome to this Week in Health it where we discuss news, information and emerging thought with leaders from across the healthcare industry. This is episode number 12. It's Friday, March 30th. Today, apple expands their move into healthcare. Is this finally the moment we've all been waiting for , an important discussion about gender pay equity within healthcare it and the priorities of the c I o.

This podcast is brought to you by Health Lyrics, a leader in moving healthcare to the cloud. To learn more, visit health lyrics.com. My name is Bill Russell, recovering Healthcare, c i o, writer and consultant with the previously mentioned health lyrics. Today I'm joined by one of the leading voices in consumer digital transformation, who is a visionary and pragmatic.

It's one of the, one of, one of the things I love about her, and it's a wonderful combination. Today I'm excited to have this, the California I for Healthcare Partners. Uh, Sarah Richardson join us. And so when I say healthcare partners, do I have to always say healthcare partners? A part of DaVita Medical Group?

Is that like taboo not to say those together. No, we are currently healthcare partners, a DaVita medical group until our close with Optum, in which case we will then have probably a new level of branding. But for now, that is how we like to, uh, coin ourselves. So that's, that's something we have in common.

So we've, we're both, I've, I've gone through a big merger and that's why I'm a former c I O and you're going through a, a, a merger now, and that's, that's a pretty exciting time. I Are you getting a feel for what it's gonna be like in the, in the new, in the new order or new world that you're entering? Yeah, no, absolutely.

And it's funny because what I love about this whole experience right now is that all of us that

Abstract

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

Keywords: Mental health services, Artificial intelligence, Machine learning, Precision mental health, Randomized clinical trials (RCTs), Precision medicine

In 1963, I was writing my first graduate paper at Columbia University on curing schizophrenia using Sarnoff Mednick’s learning theory. I was not very modest even as a first-year graduate student! But I was puzzled as to how to develop and evaluate a cure. Then, as now, the predominant research design was the randomized experiment or randomized clinical trial (RCT). It was clear that simply describing, let alone manipulating, the relevant characteristics of this one disorder and promising treatments would require hundreds of variables. Developing an effective treatment would take what seemed to me an incalculable number of randomized trials. How could we complete all the randomized experiments needed? How many different outcomes should we measure? How could we learn to improve treatment? How should we consider individual differences in these group comparisons? I am sure I was not insightful enough to think of all these questions back then, but I know I felt frustrated and stymied by